A hierarchical and adaptive deformable model for mouth boundary detection

An automatic algorithm to extract mouth boundaries in human face images is proposed. The algorithm is based on a hierarchical model adaptation scheme using deformable models. The knowledge about the shape of the object is used to define its initial deformable template. Each mouth boundary curve is initially formed based on three control points whose locations are found through an optimization process using a suitable cost functional. The cost functional captures the essential knowledge about the shape for perceptual organization. Two control points are the mouth corners, which are used as the initial location of the mouth after an approximate mouth window is found based on locating the head boundary. The model is hierarchically improved in the second stage of the algorithm. Each boundary curve is finely tuned using more control points. An old model is adaptively replaced by a new model only if a secondary cost is further reduced. The results show that the model adaptation technique satisfactorily enhances the mouth boundary model in an automated fashion.